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OLAP Systems and Multidimensional Queries I
Krzysztof Dembczynski
Intelligent Decision Support Systems Laboratory (IDSS)Poznan University of Technology, Poland
Software Development TechnologiesMaster studies, second semester
Academic year 2014/15 (winter course)
1 / 45
Review of the previous lectures
• Mining of massive datasets
• Evolution of database systems: operational vs. analytical systems.
• Dimensional modeling.
• Extraction, transformation and load of data.
2 / 45
Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
3 / 45
Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
4 / 45
OLAP systems
• The next step is to provide solutions for querying and reportingmultidimensional analytical data.
• The goal is to provide efficient solutions for physical representationand processing of these data.
5 / 45
Multidimensional reports
• OLAP servers provide an effective solution for accessing andprocessing large volumes of high dimensional data.
• OLAP systems provide tools for multidimensional reporting.
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Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
7 / 45
Multidimensional cube
• The proper data model for multidimensional reporting is themultidimensional one.
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Operators in multidimensional data model
• Roll up – summarize dataalong a dimension hierarchy.
• Drill down – go from higherlevel summary to lower levelsummary or detailed data.
• Slice and dice – correspondsto selection and projection.
• Pivot – reorient cube.
• Raking, Time functions,etc..
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Lattice of cuboids
• Different degrees of summarizations are presented as a lattice ofcuboids.
Example for the dimensions: time, product, location, supplier.
Using this structure, one can easily show roll up and drill down operations.
10 / 45
Total number of cuboids
• For an n-dimensional data cube, the total number of cuboids that canbe generated is:
T =∏
i=1,...,n
(Li + 1),
where Li is the number of levels associated with dimension i(excluding the virtual top level ”all” since generalizing to ”all” isequivalent to the removal of a dimension).
• For example, if the cube has 10 dimensions and each dimension has 4levels, the total number of cuboids that can be generated will be:
l = 510 = 9, 8× 106.
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Total number of cuboids
• Example: Consider a simple database with two dimensions:
I Columns in Date dimension: day, month, yearI Columns in Localization dimension: street, city, country.I Without any information about hierarchies, the number of all possible
group-bys is 26:
∅ ∅day street
month cityyear country
day, month ./ street, cityday, year street, country
month, year city, countryday, month, year street, city, country
12 / 45
Total number of cuboids
• Example: Consider a simple database with two dimensions:I Columns in Date dimension: day, month, yearI Columns in Localization dimension: street, city, country.
I Without any information about hierarchies, the number of all possiblegroup-bys is 26:
∅ ∅day street
month cityyear country
day, month ./ street, cityday, year street, country
month, year city, countryday, month, year street, city, country
12 / 45
Total number of cuboids
• Example: Consider a simple database with two dimensions:I Columns in Date dimension: day, month, yearI Columns in Localization dimension: street, city, country.I Without any information about hierarchies, the number of all possible
group-bys is
26:
∅ ∅day street
month cityyear country
day, month ./ street, cityday, year street, country
month, year city, countryday, month, year street, city, country
12 / 45
Total number of cuboids
• Example: Consider a simple database with two dimensions:I Columns in Date dimension: day, month, yearI Columns in Localization dimension: street, city, country.I Without any information about hierarchies, the number of all possible
group-bys is 26:
∅ ∅day street
month cityyear country
day, month ./ street, cityday, year street, country
month, year city, countryday, month, year street, city, country
12 / 45
Total number of cuboids
• Example: Consider a simple database with two dimensions:I Columns in Date dimension: day, month, yearI Columns in Localization dimension: street, city, country.I Without any information about hierarchies, the number of all possible
group-bys is 26:
∅ ∅day street
month cityyear country
day, month ./ street, cityday, year street, country
month, year city, countryday, month, year street, city, country
12 / 45
Total number of cuboids
• Example: Consider the same relations but with defined hierarchies:
I day → month → yearI street → city → countryI Many combinations of columns can be excluded, e.g. group by day,
year, street, country.I The number of group-bys is then 42:
∅ ∅year ./ country
month, year city, countryday, month, year street, city, country
13 / 45
Total number of cuboids
• Example: Consider the same relations but with defined hierarchies:I day → month → yearI street → city → country
I Many combinations of columns can be excluded, e.g. group by day,
year, street, country.I The number of group-bys is then 42:
∅ ∅year ./ country
month, year city, countryday, month, year street, city, country
13 / 45
Total number of cuboids
• Example: Consider the same relations but with defined hierarchies:I day → month → yearI street → city → countryI Many combinations of columns can be excluded, e.g. group by day,
year, street, country.I The number of group-bys is then
42:
∅ ∅year ./ country
month, year city, countryday, month, year street, city, country
13 / 45
Total number of cuboids
• Example: Consider the same relations but with defined hierarchies:I day → month → yearI street → city → countryI Many combinations of columns can be excluded, e.g. group by day,
year, street, country.I The number of group-bys is then 42:
∅ ∅year ./ country
month, year city, countryday, month, year street, city, country
13 / 45
Total number of cuboids
• Example: Consider the same relations but with defined hierarchies:I day → month → yearI street → city → countryI Many combinations of columns can be excluded, e.g. group by day,
year, street, country.I The number of group-bys is then 42:
∅ ∅year ./ country
month, year city, countryday, month, year street, city, country
13 / 45
Three types of aggregate functions
• distributive: count(), sum(),max(),min(),
• algebraic: ave(), std dev(),
• holistic: median(),mode(), rank().
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OLAP servers
• Relational OLAP (ROLAP),
• Multidimensional OLAP (MOLAP),
• Hybrid OLAP (HOLAP).
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Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
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ROLAP
• ROLAP servers use a relational or post-relational databasemanagement system to store and manage warehouse data.
• ROLAP systems use SQL and its OLAP extensions.
• Optimization techniques:I Denormalization,I Materialized views,I Partitioning,I Joins,I Indexes,I Query processing.
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ROLAP
• Advantages of ROLAP Servers:I Scalable with respect to the number of dimensions,I Scalable with respect to the size of data,I Sparsity is not a problem (fact tables contain only facts),I Mature and well-developed technology.
• Disadvantage of ROLAP Servers:I Worse performance than MOLAP,I Additional data structures and optimization techniques used to improve
the performance.
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Grouping
• Group-by is usually performed in the following way:
I Partition tuples on grouping attributes: tuples in same group areplaced together, and in different groups separated,
I Scan tuples in each partition and compute aggregate expressions.
• Two techniques for partitioningI Sorting
• Sort by the grouping attributes,• All tuples with same grouping attributes will appear together in sorted
list.
I Hashing• Hash by the grouping attributes,• All tuples with same grouping attributes will hash to same bucket,• Sort or re-hash within each bucket to resolve collisions.
• In OLAP queries use intermediate results to compute more generalgroup-bys
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Grouping
• Group-by is usually performed in the following way:I Partition tuples on grouping attributes: tuples in same group are
placed together, and in different groups separated,I Scan tuples in each partition and compute aggregate expressions.
• Two techniques for partitioningI Sorting
• Sort by the grouping attributes,• All tuples with same grouping attributes will appear together in sorted
list.
I Hashing• Hash by the grouping attributes,• All tuples with same grouping attributes will hash to same bucket,• Sort or re-hash within each bucket to resolve collisions.
• In OLAP queries use intermediate results to compute more generalgroup-bys
19 / 45
Grouping
• Group-by is usually performed in the following way:I Partition tuples on grouping attributes: tuples in same group are
placed together, and in different groups separated,I Scan tuples in each partition and compute aggregate expressions.
• Two techniques for partitioningI Sorting
• Sort by the grouping attributes,• All tuples with same grouping attributes will appear together in sorted
list.
I Hashing• Hash by the grouping attributes,• All tuples with same grouping attributes will hash to same bucket,• Sort or re-hash within each bucket to resolve collisions.
• In OLAP queries use intermediate results to compute more generalgroup-bys
19 / 45
Grouping
• Group-by is usually performed in the following way:I Partition tuples on grouping attributes: tuples in same group are
placed together, and in different groups separated,I Scan tuples in each partition and compute aggregate expressions.
• Two techniques for partitioningI Sorting
• Sort by the grouping attributes,• All tuples with same grouping attributes will appear together in sorted
list.
I Hashing• Hash by the grouping attributes,• All tuples with same grouping attributes will hash to same bucket,• Sort or re-hash within each bucket to resolve collisions.
• In OLAP queries use intermediate results to compute more generalgroup-bys
19 / 45
Grouping
• Example: Grouping by sorting (Month, City):
Month City Sale
March Poznan 105March Warszawa 135March Poznan 50April Poznan 150April Krakow 175May Warszawa 100May Poznan 70May Warszawa 75
→
Month City Sale
March Poznan 105March Poznan 50March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 100May Warszawa 75
→
Month City Sale
March Poznan 155March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 175
20 / 45
Grouping
• Example: Grouping by sorting (Month, City):
Month City Sale
March Poznan 105March Warszawa 135March Poznan 50April Poznan 150April Krakow 175May Warszawa 100May Poznan 70May Warszawa 75
→
Month City Sale
March Poznan 105March Poznan 50March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 100May Warszawa 75
→
Month City Sale
March Poznan 155March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 175
20 / 45
Grouping
• Example: Grouping by sorting (Month, City):
Month City Sale
March Poznan 105March Warszawa 135March Poznan 50April Poznan 150April Krakow 175May Warszawa 100May Poznan 70May Warszawa 75
→
Month City Sale
March Poznan 105March Poznan 50March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 100May Warszawa 75
→
Month City Sale
March Poznan 155March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 175
20 / 45
Grouping
• Example: Grouping by sorting (Month; City; Month, City):
Month City Sale
March Poznan 155March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 175
→
Month Sale
March 155March 135April 150April 175May 175May 70
→
Month Sale
March 285April 325May 245
↓City Sale
Krakow 175Poznan 155Poznan 150Poznan 70
Warszawa 135Warszawa 175
→
City Sale
Krakow 175Poznan 375
Warszawa 310
21 / 45
Grouping
• Example: Grouping by sorting (Month; City; Month, City):
Month City Sale
March Poznan 155March Warszawa 135April Krakow 175April Poznan 150May Poznan 70May Warszawa 175
→
Month Sale
March 155March 135April 150April 175May 175May 70
→
Month Sale
March 285April 325May 245
↓City Sale
Krakow 175Poznan 155Poznan 150Poznan 70
Warszawa 135Warszawa 175
→
City Sale
Krakow 175Poznan 375
Warszawa 310
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Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
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SQL queries
• Querying the star schema
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SQL queries
SQL – group by
SELECT Name, AVG(Grade)
FROM Students grades G, Student S
WHERE G.Student = S.ID
GROUP BY Name;
Name AVG(Grade)
Inmon 4.8Kimball 4.7Gates 4.0Todman 4.5
24 / 45
SQL queries
SQL– group by
SELECT Academic year, Name, AVG(Grade)
FROM Students grades G, Academic year A, Professor P
WHERE G.Professor = P.ID and G.Academic year = A.ID
GROUP BY Academic year, Name;
Academic year Name AVG(Grade)
2001/2 Stefanowski 4.22002/3 Stefanowski 4.02003/4 Stefanowski 3.92001/2 S lowinski 4.12002/3 S lowinski 3.82003/4 S lowinski 3.62003/4 Dembczynski 4.8
25 / 45
SQL queries
• OLAP extensions in SQL:
I GROUP BY ROLLUP,I GROUP BY CUBE,I GROUP BY GROUPING SETSI GROUPING and DECODE/CASEI OVERI Ranking functions
26 / 45
SQL queries
• GROUP BY CUBE
SELECT Time, Product, Location, Supplier, SUM(Gain)
FROM Sales
GROUP BY CUBE (Time, Product, Location, Supplier);
27 / 45
SQL queries
• GROUP BY CUBE
SELECT Time, Product, Location, Supplier, SUM(Gain)
FROM Sales
GROUP BY Time, Product, Location, Supplier
UNION ALL
SELECT Time, Product, Location, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Time, Product, Location
UNION ALL
SELECT Time, Product, ’’*’’, Location, SUM(Gain)
FROM Sales
GROUP BY Time, Product, Location
UNION ALL
. . .UNION ALL
SELECT ’*’, ’*’, ’*’, ’*’, SUM(Gain)
FROM Sales;
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SQL queries
• GROUP BY CUBE
SELECT Academic year, Name, AVG(Grade)
FROM Students grades GROUP BY CUBE(Academic year, Name);
Academic year Name AVG(Grade)
2001/2 Stefanowski 4.22001/2 S lowinski 4.12002/3 Stefanowski 4.02002/3 S lowinski 3.82003/4 Stefanowski 3.92003/4 S lowinski 3.62003/4 Dembczynski 4.82001/2 NULL 4.152002/3 NULL 3.852003/4 NULL 3.8NULL Stefanowski 3.9NULL S lowinski 3.6NULL Dembczynski 4.8NULL NULL 3.95
29 / 45
SQL queries
• GROUP BY ROLLUP
SELECT Time, Product, Location, Supplier, SUM(Gain)
FROM Sales
GROUP BY ROLLUP (Time, Product, Location, Supplier);
30 / 45
SQL queries
• GROUP BY ROLLUP
SELECT Time, Product, Location, Supplier, SUM(Gain)
FROM Sales
GROUP BY Time, Product, Location, Supplier
UNION ALL
SELECT Time, Product, Location, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Time, Product, Location
UNION ALL
SELECT Time, Product, ’’*’’, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Time, Product
UNION ALL
SELECT Time, ’’*’’, ’’*’’, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Time
UNION ALL
SELECT ’*’, ’*’, ’*’, ’*’, SUM(Gain)
FROM Sales;
31 / 45
SQL queries
• GROUP BY ROLLUP
SELECT Academic year, Name, AVG(Grade)
FROM Students grades G
GROUP BY ROLLUP(Academic year, Name);
Academic year Name AVG(Grade)
2001/2 Stefanowski 4.22001/2 S lowinski 4.12002/3 Stefanowski 4.02002/3 S lowinski 3.82003/4 Stefanowski 3.92003/4 S lowinski 3.62003/4 Dembczynski 4.82001/2 NULL 4.152002/3 NULL 3.852003/4 NULL 3.8NULL NULL 3.95
32 / 45
SQL queries
• GROUP BY GROUPING SETS
SELECT Time, Product, Location, Supplier, SUM(Gain)
FROM Sales
GROUP BY GROUPING SETS ((Time), (Product), (Location), (Supplier));
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SQL queries
• GROUP BY GROUPING SETS
SELECT Time, ’’*’’, ’’*’’, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Time
UNION ALL
SELECT ’’*’’, Product, ’’*’’, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Product
UNION ALL
SELECT ’’*’’, ’’*’’, Location, ’’*’’, SUM(Gain)
FROM Sales
GROUP BY Location
UNION ALL
SELECT ’*’, ’*’, ’*’, Supplier, SUM(Gain)
FROM Sales GROUP BY Supplier;
34 / 45
SQL queries
• GROUP BY GROUPING SETS
SELECT Academic year, Name, AVG(Grade)
FROM Students grades GROUP BY GROUPING SETS ((Academic year), (Name),());
Academic year Name AVG(Grade)
2001/2 NULL 4.152002/3 NULL 3.852003/4 NULL 3.8NULL Stefanowski 3.9NULL S lowinski 3.6NULL Dembczynski 4.8NULL NULL 3.95
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SQL queries
• GROUPING(<column expression>)I Returns a value of 1 if the value of expression in the row is a null
representing the set of all values.I <column expression> is a column or an expression that contains a
column in a GROUP BY clause.I GROUPING is used to distinguish the null values that are returned by
ROLLUP, CUBE or GROUPING SETS from standard null values.I The NULL returned as the result of a ROLLUP, CUBE or GROUPING SETS
operation is a special use of NULL.
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SQL queries
• GROUPING(<column expression>)
SELECT Extra scholarship, AVG(Grade), GROUPING(Extra scholarship) as
Grouping
FROM Students grades
GROUP BY ROLL UP(Extra scholarship);
Extra scholarship AVG(Grade) Grouping
Yes 4.15 0No 3.61 0NULL 4.03 0NULL 3.89 1
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SQL queries
• DECODE(expression , search , result [, search ,result]... [, default] )
I If the value of expression is equal to search, then result isreturned, otherwise default is returned.
I The functionality is similar to CASE expression,I The results of GROUPING() can be passed into a DECODE function or
the CASE expression.
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SQL queries
• DECODE(expression , search , result [, search ,
result]... [, default] )
SELECT DECODE(GROUPING(Extra scholarship), 1, "Total Average",
Extra scholarship) as Extra scholarship, AVG(Grade)
FROM Students grades
GROUP BY ROLL UP(Extra scholarship);
Extra scholarship AVG(Grade)
Yes 4.15No 3.61NULL 4.03Total average 3.89
37 / 45
SQL queries
• OVER():
I Determines the partitioning and ordering of a rowset before theassociated window function is applied.
I The OVER clause defines a window or user-specified set of rows within aquery result set.
I A window function then computes a value for each row in the window.I The OVER clause can be used with functions to compute aggregated
values such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():I Determines the partitioning and ordering of a rowset before the
associated window function is applied.
I The OVER clause defines a window or user-specified set of rows within aquery result set.
I A window function then computes a value for each row in the window.I The OVER clause can be used with functions to compute aggregated
values such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():I Determines the partitioning and ordering of a rowset before the
associated window function is applied.I The OVER clause defines a window or user-specified set of rows within a
query result set.
I A window function then computes a value for each row in the window.I The OVER clause can be used with functions to compute aggregated
values such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():I Determines the partitioning and ordering of a rowset before the
associated window function is applied.I The OVER clause defines a window or user-specified set of rows within a
query result set.I A window function then computes a value for each row in the window.
I The OVER clause can be used with functions to compute aggregatedvalues such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():I Determines the partitioning and ordering of a rowset before the
associated window function is applied.I The OVER clause defines a window or user-specified set of rows within a
query result set.I A window function then computes a value for each row in the window.I The OVER clause can be used with functions to compute aggregated
values such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():I Determines the partitioning and ordering of a rowset before the
associated window function is applied.I The OVER clause defines a window or user-specified set of rows within a
query result set.I A window function then computes a value for each row in the window.I The OVER clause can be used with functions to compute aggregated
values such as moving averages, cumulative aggregates, running totals,or a top N per group results.
I Syntax:OVER (
[ <PARTITION BY clause> ]
[ <ORDER BY clause> ]
[ <ROW or RANGE clause> ]
)
38 / 45
SQL queries
• OVER():
I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:
• Defines the logical order of the rows within each partition of the resultset, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:
• Defines the logical order of the rows within each partition of the resultset, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:
• Defines the logical order of the rows within each partition of the resultset, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:
• Defines the logical order of the rows within each partition of the resultset, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:
• Further limits the rows within the partition by specifying start and endpoints within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:• Further limits the rows within the partition by specifying start and end
points within the partition.
• This is done by specifying a range of rows with respect to the currentrow either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:• Further limits the rows within the partition by specifying start and end
points within the partition.• This is done by specifying a range of rows with respect to the current
row either by logical association or physical association.
• The ROWS clause limits the rows within a partition by specifying a fixednumber of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
39 / 45
SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:• Further limits the rows within the partition by specifying start and end
points within the partition.• This is done by specifying a range of rows with respect to the current
row either by logical association or physical association.• The ROWS clause limits the rows within a partition by specifying a fixed
number of rows preceding or following the current row.
• The RANGE clause logically limits the rows within a partition byspecifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
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SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:• Further limits the rows within the partition by specifying start and end
points within the partition.• This is done by specifying a range of rows with respect to the current
row either by logical association or physical association.• The ROWS clause limits the rows within a partition by specifying a fixed
number of rows preceding or following the current row.• The RANGE clause logically limits the rows within a partition by
specifying a range of values with respect to the value in the current row.
• Preceding and following rows are defined based on the ordering in theORDER BY clause.
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SQL queries
• OVER():I PARTITION BY:
• Divides the query result set into partitions. The window function isapplied to each partition separately and computation restarts for eachpartition.
I ORDER BY:• Defines the logical order of the rows within each partition of the result
set, i.e., it specifies the logical order in which the window functioncalculation is performed.
I ROW | RANGE:• Further limits the rows within the partition by specifying start and end
points within the partition.• This is done by specifying a range of rows with respect to the current
row either by logical association or physical association.• The ROWS clause limits the rows within a partition by specifying a fixed
number of rows preceding or following the current row.• The RANGE clause logically limits the rows within a partition by
specifying a range of values with respect to the value in the current row.• Preceding and following rows are defined based on the ordering in the
ORDER BY clause.
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SQL queries
• Ranking functions:
I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:
• Returns the rank of rows within the partition of a result set, withoutany gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:
• Returns the rank of rows within the partition of a result set, withoutany gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:
• Returns the rank of rows within the partition of a result set, withoutany gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:
• Returns the rank of rows within the partition of a result set, withoutany gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:• Returns the rank of rows within the partition of a result set, without
any gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:• Returns the rank of rows within the partition of a result set, without
any gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:
• Distributes the rows in an ordered partition into a specified number ofgroups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:• Returns the rank of rows within the partition of a result set, without
any gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:• Distributes the rows in an ordered partition into a specified number of
groups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:• Returns the rank of rows within the partition of a result set, without
any gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:• Distributes the rows in an ordered partition into a specified number of
groups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:
• Returns the sequential number of a row within a partition of a resultset, starting at 1 for the first row in each partition.
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SQL queries
• Ranking functions:I RANK () OVER:
• Returns the rank of each row within the partition of a result set. Therank of a row is one plus the number of ranks that come before the rowin question.
I DENSE RANK () OVER:• Returns the rank of rows within the partition of a result set, without
any gaps in the ranking. The rank of a row is one plus the number ofdistinct ranks that come before the row in question.
I NTILE (integer expression) OVER:• Distributes the rows in an ordered partition into a specified number of
groups. The groups are numbered, starting at one. For each row,NTILE returns the number of the group to which the row belongs.
I ROW NUMBER () OVER:• Returns the sequential number of a row within a partition of a result
set, starting at 1 for the first row in each partition.
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SQL queries
• Examples:I Ranking of the students
SELECT Student, Avg(Grade), RANK () OVER (ORDER BY Avg(Grade) DESC)
FROM Students grades GROUP BY Student;
I To sort according to rank, we need to order the resulting relation:
SELECT Student, Avg(Grade), RANK () OVER (ORDER BY Avg(Grade) DESC) AS
rank of grades
FROM Students grades GROUP BY Student ORDER BY rank of grades;
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SQL queries
• Examples:I Ranking of students partitioned by instructors.
SELECT Instructor Name, Student, Avg(Grade), RANK () OVER (PARTITION BY
Instructor Name ORDER BY Avg(Grade) DESC) AS rank 1
FROM Students grades
GROUP BY Student,Instructor Name
ORDER BY Instructor Name, rank 1;
I Moving average of a student:
SELECT Student, Academic year, AVG (grades) OVER (PARTITION BY Student
ORDER BY Academic year DESC ROWS UNBOUNDED PRECEDING)
FROM Students grades
ORDER BY Student, Academic year;
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Outline
1 Motivation
2 OLAP Servers
3 ROLAP
4 SQL
5 Summary
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Summary
• OLAP Systems: Relational OLAP.
• SQL for analytical queries.
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Bibliography
• J. Han and M. Kamber. Data Mining: Concepts and Techniques.
Morgan Kaufmann Publishers, second edition, 2006
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